The optical implementation of computing systems, whose structure and function are motivated by natural intelligence systems, would be a unique path to optical computing and to neural network models for computation. Electronic interconnects are becoming the bottleneck in the realization of neural networks and other globally interconnected systems. The heat dissipation and interconnection delay are also serious design performance limiting factors. One possible solution is to use free space optical interconnects as implemented, for example, in volume holograms recorded in photorefractive crystals. With optics, it is possible to have large arrays of processing elements communicating with one another without wire interconnections.
The optical implementation of a neural network consists of two basic components: neurons and interconnections. The neuron is an optical non-linear processing element (e.g., threshold unit) that can be implemented by a single switching device. The practical neural computer may require millions of neurons operating in parallel. Each neuron accepts inputs from other neurons and produces a single output that is connected to many other neurons, typically several thousands. Hence, the number of interconnections in a network is much larger than the number of neurons. While this massive connectivity is relatively difficult to achieve electronically, optics is a practical means which is suitable for the realization of interconnects. This fact provides the main motivation for considering the optical implementation of neural networks.
Several emerging optical technologies have been considered for the realization of neural networks: spatial light modulators (SLMs), integrated optoelectronics, and arrays of non-linear optical switches.
SLMs have been investigated primarily for optical image processing. However, practical, useful devices are not yet available except for laboratory experiments.
The arrays of non-linear optical switches are intended either for optical communications or digital optical computing. The major problem with these arrays is the high power required to switch each element and the sensitivity of their operation to environmental conditions.
One of the most promising implementation technology appears to be optoelectronics. The optoelectronic approach to simulating an array of neurons involves monolithic integration of two-dimensional (2-D) arrays of photodetectors and light sources on a single chip. The output of each detector is connected to the corresponding light source via a saturating amplifier or an appropriate analog circuit that performs the required non-linear mapping. Light emitted from a particular neuron may be diffracted to many other neurons in a programmable fashion by gratings written in holographic optical elements, as an example. The only electrical connection would be the power and the global bias to optoelectronic integrated circuits (OEICs).
Since one of the requirements for a neuron is a detector and a saturating amplifier, a double heterojunction phototransistor (DHPT) is a likely candidate because of its structural compatibility with laser diodes and light emitting diodes (LEDs) and its ability to detect light and to provide high gain in compensating for the losses incurred in the holographic portion of the system. Assuming a detector efficiency of 0.4 A/W, a LED efficiency of 0.01 W/A to be used as the light source, and a hologram diffraction efficiency of 10%, the requirement that the loop gain of the system must be at least 1 implies that the transistor must provide a current gain of at least 2,500. To achieve this, high-gain double heterojunction bipolar transistors (DHBTs) must be developed.
Integration of a double heterostructure bipolar transistor and an injection laser is known; see, e.g., J. Katz et al, "A Monolithic Integration of GaAs/AlGaAs Bipolar Transistor and Heterostructure Laser", Applied Physics Letters, Vol. 37, pp. 211-213 (1980). No disclosure or suggestion as to use in neural networks is made, however.
There remains a need for integration of a high-gain double heterojunction phototransistor and a light source for use in constructing neurons for implementation of optical neural networks.